In 2020, the global stock market had 155.7 million trades per day worth $554.6 billion dollars.
The market prices you see at any given moment reflect the buying and selling by millions of market participants incorporating all known information about a company. Collectively, these market participants are highly educated and highly motivated. As a result, there is a lot of information that is quickly incorporated into prices.
Listen now and learn:
- Why market prices fluctuate
- How financial markets are like piles of sand
- The biggest mistake people make when thinking about making a change to their portfolio
Episode
Outline
My father-in-law, Tom, is the king of buying and selling used cars. There was a period of time where it seemed like every two or three years he’d sell his car and buy another used one. And if he didn’t come out ahead every time, he seemed to at least break even.
For years I’ve been using Tom and his cars as a simplified example of how markets work.
Let’s say Tom offers to buy my car for $10,000 and then sells it to my neighbor Bill for $20,000. In this scenario, Tom doubled his money overnight – but he probably couldn’t make that kind profit if Bill and I had access to market data on used car pricing.
If I had such data, I could take Tom’s offer and compare it to other prices that cars like mine have recently sold for (thanks to the ability to easily research this information online). Maybe I could see used cars like mine usually sold for $19,000 to $21,000.
Let’s pretend I needed cash fast, so I’d agree to sell the car to Tom for $19,000. If I had more time to be patient, I could probably find a buyer and sell the car for a higher price, but my need for a quick transaction for whatever reason prompts me to sell near the lower range of what I think I could get if I had been patient.
Tom could then go to Bill, who also had access to market data on used cars. Bill could see that the most recent transaction for a car of my make and model was $19,000. If Bill wasn’t in a hurry to buy a car, he won’t buy from Tom for $20,000.
Instead, Bill could choose to wait until someone offers him a similar car for $19,000 or until Tom drops the price.
Back to the Basics: Price and the Impact of Supply and Demand
In real life, there are likely to be more buyers and sellers in the used car market than just Tom, Bill, and myself. This could impact what I sell my car to Tom for, and what Bill is willing to buy it for even when we all have access to the same data on the market.
If the market had lots of sellers and a shortage of buyers, Bill could likely get the car for a lower price – from Tom or someone else willing to accept less profit. If Tom had the only car and there were many buyers, though, he would have the advantage and could sell at a higher price.
This example demonstrates a very simple market in which participants with different objectives and varying levels of information set prices through the basic forces of supply and demand.
The more people that participate in the market, the more information that gets incorporated into the eventual transaction price and the more confident we are in that price being “fair.”
In Tom’s case, he had a good chance of “beating the market” when Bill and I were the only other participants, particularly if we didn’t have information from the internet at our disposal. But if more buyers and sellers of used cars enter the market, Tom will have less of an opportunity to make a big profit.
It used to be easier to outsmart the used car market and flip cars the way my father-in-law did, but competition for used car profits is high and the amount of available information to inform buyers and sellers has skyrocketed in the past decade.
So what does all this have to do with the stock market?
Why Beating the Market Is Harder Than You Think
The used car example shows a simple example of how supply and demand work to set prices in a marketplace.
I find that people tend to respect the power of supply and demand in markets in their everyday life, but that respect seems to break down when it comes to financial markets. The stock market is much like any other market of buyers and sellers.
In 2020, the global stock market had 155.7 million trades per day worth $554.6 billion dollars.
The market prices you see at any given moment reflect the buying and selling by millions of market participants incorporating all known information about a company. Collectively, these market participants are highly educated and highly motivated. As a result, there is a lot of information that is quickly incorporated into prices.
When I talk to an investor who wants to make a change to their portfolio – whether that’s putting money to work, pulling money out of the stock market, buying or selling a specific investment, whatever – these investors are trying to beat the market. They never say that or think of it that way, but that’s exactly what is happening when you deviate from a long-term financial plan and asset allocation.
While it’s natural to want to make investment decisions that lead you to beat the market, doing so often shows a lack of awareness for how markets work, and it also dramatically underestimates the competition within financial markets and overestimate the opportunity for excess return.
Think about it this way: Market Prediction Is Harder Than You Think
Imagine The Wall Street Journal ran a contest for its subscribers in which each subscriber viewed 100 photos of various men and women. To participate in the contest, each subscriber must pick the six most attractive people. The subscriber that choses the photos voted for most often will win $1 million.
How would you win the contest?
At first blush, it may seem obvious to simply pick the six people you find most attractive. But the contest doesn’t reward the person who picks the most attractive people. It rewards the person that chooses the most popular photos.
This thought experiment was originally presented by famed economist John Maynard Keynes in his 1936 seminal work The General Theory of Employment, Interest and Money.
To win this contest, you must determine which person the average subscriber finds attractive. The challenge, however, is that many other competitors understand this as well. To this, Keynes says:
“We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be. And there are some, I believe, who practice the fourth, fifth and higher degrees.”
Using the example of the newspaper contest, which is known in the field of economics as “the beauty contest,” Keynes is explaining the type of thinking required of anticipating market movements.
“Winning” the game of market prediction requires more than calculating an expected return; it requires the psychological task of understanding how the crowd perceives the crowd’s perception.
Whether you acknowledge it or not, we all live in a bubble of sorts. It’s hard to really have a grasp on the average perspective of market participants.
Most Investors Don’t Have an Edge (That Includes You)
All too often I hear some form of the following:
“I’m worried about __________ and think the market is going to crash. I want to reduce my exposure to stocks or make portfolio changes to prepare.”
That blank space is always something different. Politics, the dollar, national debt, monetary policy, entitlement system, war, valuations, market highs, interest rates, Eurozone, China, etc. Most people fill in the blank with something specific after reading popular print or internet publications, listening to “pundits,” or tuning in to social chatter.
There are two problems with this way of thinking:
The first is that this line of thinking entirely ignores the crowd’s perception.
When’s the last time you’ve thought about the people making up the crowd?
If you only read, listen, or watch to a limited set of news sources – you’re missing out on what the crowd perceives. A few weeks ago, Josh Brown said something on CNBC that really resonated with me:
“The majority of the financial media is still talking to most market participants’ parents.”
While I wouldn’t use those exact words, I agree with the sentiment.
In 2020, the Baby Boomer Generation (those born from 1946-1964) made up 21.45% of the population. The Silent Generation (those born from 1928 to 1945) makes up 6.61% of the population. So together they make up 28.06% of the population.
Meanwhile, Generation X and Millennials combine to make up 41.64% of the population. Generation Z (20.35%0 is now has now two sets of graduates that have entered the workforce.
Simply put, Gen X and Millennials dominate the market. If you add in the fact that these two generations make up the vast majority of decision makers at major institutional investors, then perhaps you can start to understand how traditional media outlets (which tends to cater to an older demographic) might not give you an accurate representation of what the overall market’s perception of things are.
So when you’re thinking about how markets work, you have to first understand who makes up markets and the required level 2 or level 3 thinking that is required to beat the market’s forces.
Again, in my experience, people that make changes in their portfolios for some fill in the blank reason (politics, valuations, inflation, whatever) are generally not thinking about what the average market participant thinks the average market participant thinks.
The second issue with this sort of cause and effect thinking is that it oversimplifies the many variables that impact market movements.
The stock market is a complex adaptive system in which linear thinking – A causes B – isn’t sufficient. Most non-professional investors (along with many professionals) use linear reasoning when thinking about the stock market because the human brain is obsessed with precisely linking cause and effect.
Deeper Reading: Thinking About Markets Like Piles of Sand
The idea of complex adaptive systems might sound overwhelming, but there was an experiment conducted by physicist Per Bak that makes the concept easier to understand.
If you drop one grain of sand at a time onto an empty table, a small, cone-shaped pile begins to form. As the pile grows, eventually a grain of sand will hit the pile and trigger an avalanche.
If you’ve ever watched sand run through an hourglass, you might have noticed this dynamic in action. As the sand pours through the top glass and the pile below grows, small avalanches of sand start cascading down the side of the cone-shaped pile.
The longer the pile avoids an avalanche, the bigger the eventual sand avalanche will be.
Bak’s designed this experiment to determine the exact conditions that would trigger that avalanche, but he found the sandpile to be completely unpredictable. Avalanches sometimes occurred after hundreds of grains were added. Sometimes they happened after thousands.
What Bak came to realize was that the timing of an avalanche was not a function of the size of the pile or number of grains of sand, but instead was related to the interactions between those individual grains of sand.
The more grains of sand in the pile, the more interactions that occur and the more difficult it is to predict the next avalanche.
Eventually, the pile reaches a critical point in which the pile transforms into something more complex and gains properties that must be considered separately from the individual pieces (or said different, it becomes something greater than the sum of its parts)
A sandpile is a complex adaptive system in which you can’t study the individual grains of sand and understand the pile in its entirety.
You never know which grain of sand is going to cause an avalanche or how big an eventual avalanche will be because each grain of sand uniquely interacts with other grains to create a pile that is slightly different each time, even if the inputs and conditions are exactly the same.
Like piles of sand, financial markets are also nonlinear systems. But they are far more complex. Sandpiles are simply made up of interacting grains of sand. Financial markets are comprised of millions of interconnected inputs that adapt and learn over time.
We can’t predict when an avalanche is going to occur in a sandpile with only one set of interactive agents. Shouldn’t we expect it to be exponentially more challenging, if not impossible, to predict financial markets comprised of human and computers each seeking out different data sources and interpreting them based on their unique personal experiences and trading rules?
The answer is yes, we should expect to find those predictions nearly impossible. And yet average investors and countless talking heads insist on making market predictions.
Understanding the Financial Markets as Complex Adaptive Systems
The times I find myself referring to financial markets as complex adaptive systems most often are cases in which people try to precisely link cause and effect.
Our innate human tendency to seek clean-cut reasons behind everything around us makes us highly susceptible to linear thinking. The financial media and a lot of people working for Wall Street make this worth by giving a platform to “experts” that succinctly describe past events by explaining specific causes that led to specific market movements, all after the fact.
There is a quote from Per Bak’s book titled How Nature Works that reminds me a lot of Wall Street pundits:
“Historians explain events in a narrative language where event A leads to event B and C leads to D. Then, because of event D, event B leads to E. However, if the event C had not happened, then D and E would not have happened. The course of history would have changed into another sequence of events, which would have been equally well explainable, in hindsight, with a different narrative.”
The point isn’t that cause and effect don’t exist, but that they aren’t proportional. Large fluctuations are more the result of the interactions within the complex adaptive system and less attributable to external or environmental factors.
This means that cause and effect are not neatly linked. As a result, worrying about the cause of the next crisis is a futile exercise.
If we start thinking of financial markets more like piles of sand, then we can no longer assume that a given action or event will produce a given reaction. Thinking about markets in a nonlinear fashion requires us to embrace an ever-changing mix of calculus and psychology.
I began this episode by describing a simple used car market with three people. Changes in supply and demand drives all changes in market prices, but the dynamics driving supply and demand is far more complex than most people realize.
The market is greater than the sum of its parts. You can take all the fundamental research in the world and have your finger on the pulse of the market, but the properties that emerge will always be difficult to predict.
This is a big part of what makes forecasting in financial markets so difficult. People place too much importance on explaining individual pieces of the market and not enough on how people perceive those pieces will interact with each other.
Even if you are aware of this dynamic, very few people have the capability to master the ever-changing mix of calculus and psychology.
Although the odds of any investor consistently outsmarting the market are very slim, that doesn’t mean you shouldn’t invest. It simply suggests that actively trying to beat the market isn’t a smart strategy if you want to focus on building wealth over the long-term.
Rather than compete with the market, you can improve your chances for success by using a strategy that lets the market work for you.
Resources
- Market Prediction is Harder Than You Think
- Thinking About Markets Like Piles of Sand
- Materials, energy lag in today’s market with Josh Brown on CNBC
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Long term investing made simple. Most people enter the markets without understanding how to grow their wealth over the long term or clearly hit their financial goals. The Long Term Investor shows you how to proactively minimize taxes, hedge against rising inflation, and ride the waves of volatility with confidence.
Hosted by the advisor, Chief Investment Officer of Plancorp, and author of “Making Money Simple,” Peter Lazaroff shares practical advice on how to make smart investment decisions your future self with thank you for. A go-to source for top media outlets like CNBC, the Wall Street Journal, and CNN Money, Peter unpacks the clear, strategic, and calculated approach he uses to decisively manage over 5.5 billion in investments for clients at Plancorp.
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